# First, let's import NumPy (the standard convention is to use 'np')
import numpy as np

# Let's check the version
print("NumPy version:", np.__version__)

# Example: Creating arrays from lists
arr1 = np.array([1, 2, 3, 4, 5])
print("1D array:", arr1)

arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print("2D array:\n", arr2)

# Example: Creating arrays with built-in functions
zeros = np.zeros(5)
print("Zeros:", zeros)

ones = np.ones((2, 3))
print("Ones:\n", ones)

range_arr = np.arange(0, 10, 2)
print("Range array:", range_arr)

linspace_arr = np.linspace(0, 1, 5)
print("Linspace array:", linspace_arr)

# EXERCISE 1: Your turn!
# Create the following arrays:
# 1. A 1D array with numbers [10, 20, 30, 40, 50]
exercise1_1 = # Your code here

# 2. A 2D array filled with zeros, shape (3, 4)
exercise1_2 = # Your code here

# 3. An array with numbers from 5 to 15 (exclusive), step 2
exercise1_3 = # Your code here

# Example: Exploring array properties
example_arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

print("Array:\n", example_arr)
print("Shape:", example_arr.shape)        # Dimensions
print("Size:", example_arr.size)          # Total number of elements
print("Data type:", example_arr.dtype)    # Data type
print("Number of dimensions:", example_arr.ndim)

# EXERCISE 2: Your turn!
# Create a 3D array with shape (2, 3, 4) filled with ones
exercise2_arr = # Your code here

# Example: Accessing elements
arr = np.array([10, 20, 30, 40, 50])

print("Original array:", arr)
print("First element:", arr[0])
print("Last element:", arr[-1])
print("Elements 1 to 3:", arr[1:4])

# Example: 2D array indexing
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print("2D array:\n", arr_2d)
print("Element at row 1, col 2:", arr_2d[1, 2])
print("First row:", arr_2d[0, :])
print("Last column:", arr_2d[:, -1])

# EXERCISE 3: Your turn!
test_arr = np.array([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]])

# 1. Get the element in the middle row, middle column
exercise3_1 = # Your code here

# 2. Get the last row
exercise3_2 = # Your code here

# 3. Get the first two columns of all rows
exercise3_3 = # Your code here

# Example: Element-wise operations
a = np.array([1, 2, 3, 4])
b = np.array([5, 6, 7, 8])

print("Array a:", a)
print("Array b:", b)
print("Addition:", a + b)
print("Multiplication:", a * b)
print("Power:", a ** 2)
print("Square root:", np.sqrt(a))

# Example: Operations with scalars
print("a + 10:", a + 10)
print("a * 3:", a * 3)

# EXERCISE 4: Your turn!
x = np.array([2, 4, 6, 8])
y = np.array([1, 3, 5, 7])

# 1. Calculate x - y
exercise4_1 = # Your code here

# 2. Calculate x divided by 2
exercise4_2 = # Your code here

# 3. Calculate the square of y
exercise4_3 = # Your code here

# Example: Statistical functions
data = np.array([1, 5, 3, 9, 2, 8, 4, 7, 6])

print("Data:", data)
print("Mean:", np.mean(data))
print("Median:", np.median(data))
print("Standard deviation:", np.std(data))
print("Min:", np.min(data))
print("Max:", np.max(data))
print("Sum:", np.sum(data))

# Example: Finding indices
print("Index of max value:", np.argmax(data))
print("Index of min value:", np.argmin(data))

# EXERCISE 5: Your turn!
scores = np.array([85, 92, 78, 96, 88, 79, 91, 84])

# 1. Calculate the average score
exercise5_1 = # Your code here

# 2. Find the highest score
exercise5_2 = # Your code here

# 3. Find how many scores are above 85
exercise5_3 = # Your code here (Hint: use np.sum() with a condition)

# Example: Changing array shape
original = np.arange(12)
print("Original array:", original)

reshaped = original.reshape(3, 4)
print("Reshaped to 3x4:\n", reshaped)

flattened = reshaped.flatten()
print("Flattened:", flattened)

# Example: Adding dimensions
arr_1d = np.array([1, 2, 3])
arr_2d = arr_1d.reshape(-1, 1)  # -1 means "figure out this dimension"
print("1D to column vector:\n", arr_2d)

# EXERCISE 6: Your turn!
numbers = np.arange(24)

# 1. Reshape to a 4x6 array
exercise6_1 = # Your code here

# 2. Reshape to a 2x3x4 array
exercise6_2 = # Your code here

# Example: Filtering with conditions
arr = np.array([1, 5, 3, 8, 2, 9, 4])

print("Original array:", arr)

# Create boolean mask
mask = arr > 4
print("Mask (elements > 4):", mask)

# Apply mask
filtered = arr[mask]
print("Filtered array:", filtered)

# One-liner
big_numbers = arr[arr > 4]
print("Numbers > 4:", big_numbers)

# EXERCISE 7: Your turn!
temperatures = np.array([20, 25, 18, 30, 22, 35, 28, 15])

# 1. Find temperatures above 25
exercise7_1 = # Your code here

# 2. Count how many temperatures are below 20
exercise7_2 = # Your code here

# Example: A mini data analysis
# Let's analyze some fake test scores
np.random.seed(42)  # For reproducible results
class_scores = np.random.normal(78, 12, 30)  # 30 students, mean=78, std=12

print("Class scores (first 10):", class_scores[:10])
print(f"Class average: {np.mean(class_scores):.2f}")
print(f"Number of students who passed (≥70): {np.sum(class_scores >= 70)}")
print(f"Highest score: {np.max(class_scores):.2f}")
print(f"Lowest score: {np.min(class_scores):.2f}")

# FINAL EXERCISE: Your turn!
# Create your own mini analysis
np.random.seed(123)
sales_data = np.random.randint(100, 1000, 20)  # 20 days of sales

print("Daily sales:", sales_data)

# 1. Calculate total sales for the 20 days
total_sales = # Your code here

# 2. Find the average daily sales
avg_sales = # Your code here

# 3. Find days with sales above average
above_avg_days = # Your code here

# 4. What's the best sales day?
best_day = # Your code here (hint: use np.argmax() + 1 for day number)

print(f"Analysis Results:")
print(f"Total sales: ${total_sales}")
print(f"Average daily sales: ${avg_sales:.2f}")
print(f"Days with above-average sales: {len(above_avg_days)}")
print(f"Best sales day: Day {best_day}")

Interactive Introduction to NumPy¶

A hands-on tutorial with examples and exercises¶

1. GETTING STARTED WITH NUMPY¶

2. CREATING ARRAYS¶

3. ARRAY ATTRIBUTES¶

4. INDEXING AND SLICING¶

5. ARRAY OPERATIONS¶

6. USEFUL FUNCTIONS¶

7. RESHAPING ARRAYS¶

8. BOOLEAN INDEXING¶

9. PUTTING IT ALL TOGETHER¶

BONUS: Quick Reference¶